Microblogging services (such as Twitter) are the representative information communication networks during
the Web 2.0 era, which have gained remarkable popularity. Weibo has become a popular platform for
information dissemination in online social networks due to its large number of users. In this study, a microblog
information dissemination model is presented. Related concepts are introduced and analyzed based on the
dynamic model of infectious disease, and new influencing factors are proposed to improve the susceptibleinfective-
removal (SIR) information dissemination model. Correlation analysis is conducted on the existing
information dissemination risk and the rumor dissemination model of microblog. In this study, web hyper is
used to model rumor dissemination. Finally, the experimental results illustrate the effectiveness of the method
in reducing the rumor dissemination of microblogs.

This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which has
been actively researched in academia. Most existing works have adopted traditional supervised machine
learning methods to analyze emotions in microblogs; however, these approaches may not be suitable in
Chinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method that
mines associated microblog emotions based on a popular microblog through user-building combined with
spectral clustering to analyze microblog content. Experimental results for a public microblog benchmark
corpus show that the proposed method can improve identification accuracy and save manually labeled time
compared to existing methods.

Recently, with the development of Internet technologies and propagation of smart devices, use of microblogs
such as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new information
on microblogs because the content on their timelines is continually updating. Therefore, clustering algorithms
are necessary to arrange the content of microblogs by grouping them for a user who wants to get the newest
information. However, microblogs have word limits, and it has there is not enough information to analyze for
content clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not only
measures the similarity between the data represented as a vector space model, but also measures the semantic
similarity between the data by exploiting the TagCluster for clustering. Through the experimental results on
the RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clustering
algorithm.

Indexing

JIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

This journal was supported by the Korean Federation of Science and Technology Societies Grant funded by the Korean Government (Ministry of Education).

Society

ABOUT THE SOCIETY

Ever since information processing became one of the most important industries in the country, computing professionals have encountered a growing number of challenges.
Along with scholars and colleagues in related fields, they have gathered together at a variety of forums and meetings over the last few decades to share their knowledge and experiences,
and the outcomes of their research. These exchanges led to the founding of the Korea Information Processing Society (KIPS) on January 15, 1993. The KIPS was registered as an incorporated association under the Ministry of Science,
ICT and Future Planning under the government of the Republic of Korea. The main purpose of the KIPS organization is to improve our society by achieving the highest capability possible in the domain of information technology.
As such, it focuses on close collaboration with the nationâs industry, academic, and research communities to foster technological innovation,
to enhance its members' careers, and to promote the advanced information processing industry.